Robot navigation using 2D and 3D path planning

ABSTRACT

Methods, systems, and apparatus, including computer-readable storage devices, for robot navigation using 2D and 3D path planning. In the disclosed method, a robot accesses map data indicating two-dimensional layout of objects in a space and evaluates candidate paths for the robot to traverse. In response to determining that the candidate paths do not include a collision-free path across the space for a two-dimensional profile of the robot, the robot evaluates a three-dimensional shape of the robot with respect to a three-dimensional shape of an object in the space. Based on the evaluation of the three-dimensional shapes, the robot determines a collision-free path to traverse through the space.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 15/967,805, filed May 1, 2018, which is incorporated by reference.

TECHNICAL FIELD

This disclosure relates generally to robot navigation.

BACKGROUND

As robots move about a space, they may need to navigate around obstaclesand objects in their environment.

SUMMARY

A robot can dynamically switch between two-dimensional (2D) pathplanning and three-dimensional (3D) path planning based on conditionsencountered. For example, the robot can use a 2D path planner most ofthe time for computational efficiency. The robot's 2D planner can use a2D occupancy grid, similar to a floor plan, to map positions of objectsin a space. The 2D planner further uses a 2D profile of the robot, e.g.,a projection of the robot onto the plane of the floor, to determinewhether there is a collision-free path for the robot around the objectsin the space. If the robot identifies a collision-free path using the 2Doccupancy grid, the robot can safely navigate using the 2D techniquewhile avoiding the higher computational demands of 3D analysis ofpotential obstacles.

If the robot is unable to identify a collision-free path using 2Danalysis, the robot may temporarily switch to 3D path planning toperform a more fine-grained assessment whether a collision-free pathexists. The 3D techniques use more computational resources, but allowthe robot to more accurately evaluate its ability to navigate confinedspaces. The robot can detect specific scenarios or path segments where3D path planning is used. This includes detecting when a maximum 2Dprofile of the robot (e.g., indicating the maximum width of the robot)does not fit through an area, but a minimum 2D profile of the robot(e.g., indicating a minimum width of the robot) does fit through thearea. This analysis can conserve power and computation resources byusing 3D path planning only when a potential path exists. If even theminimum dimensions of the robot do not fit through an area, unnecessarycomputation for 3D path planning is avoided when the 2D path planningshows no possible path exists.

The use of complementary 2D and 3D path planning increases thecapability of the robot to navigate complex environments whilemaintaining efficiency. For example, for a round table in a room, theentire area of the table top would be shown as blocked in a 2D occupancygrid. However, the base of the table may be significantly narrower thanthe table top, narrow enough for the robot to pass by while a portion ofthe robot extends slightly under or over the table top. The 3D analysiscompares the vertical profile of the robot with the vertical profile ofthe table to determine whether the robot can safely pass withoutcolliding with the table. In addition, the 3D analysis can alsodetermine how to change the pose of the robot, e.g., to raise or lower arobot arm or other component, to change the vertical profile of therobot and provide clearance in the narrow space.

In some implementations, the method is performed by a robot and includes(i) accessing, by the robot, map data indicating a 2D layout of objectsin a space; (ii) evaluating, by the robot, candidate paths for the robotto traverse the space based on the map data; (iii) determining, by therobot, that the candidate paths do not include a collision-free pathacross the space for a 2D profile of the robot; (iv) in response to thedetermination, evaluating, by the robot, a three-dimensional shape ofthe robot with respect to a 3D shape of an object in the space; and (v)determining, by the robot, a path based on evaluating the 3D shape ofthe robot with respect to the 3D shape of an object in the space.

In some implementations, the 2D profile of the robot is a minimum 2Dprofile of the robot.

In some implementations, the method includes evaluating candidate pathsfor the robot to traverse the space based on the map data bydetermining, by the robot, a location of an object in the space and anarea occupied by the object and generating, by the robot, a path segmentfor the robot to traverse, wherein the path segment does not intersectthe area occupied by the object. In some implementations, the robot candetermine that the candidate paths do not include a collision-free pathacross the space for a 2D profile of the robot by (i) generating a 2Darea along the path segment by tracing a 2D profile of the robot along alength of the path segment and (ii) determining that the area along thepath segment intersects the area occupied by the object.

In some implementations, the 3D shape of the robot is a first 3D shapecorresponding to a first configuration of the robot, and determining apath based on evaluating the 3D shape of the robot with respect to the3D shape of an object in the space includes (i) based on evaluating thefirst 3D shape of the robot with respect to the 3D shape of an object inthe space, determining, by the robot, that the first 3D shape of therobot intersects the 3D shape of the object, and (ii) in response to thedetermination, determining a path based on evaluating, by the robot, asecond 3D shape of the robot with respect to the 3D shape of the object,the second 3D shape of the robot corresponding to a second configurationof the robot.

In some implementations, accessing map data indicating a 2D layout ofobjects in a space includes receiving, by the robot, map data from aserver over a communication network.

In some implementations, evaluating candidate paths for the robot totraverse the space based on the map data includes evaluating, by therobot, more than one candidate path for the robot to traverse the space.In such implementations, the robot can determine that the candidatepaths do not include a collision-free path across the space for a 2Dprofile of the robot by determining that none of the more than onecandidate path include a collision-free path across the space.

In some implementations, the described methods and operations can beperformed by a robot. In some implementations, one or morenon-transitory computer-readable media can store instructions that, whenexecuted by one or more computers, cause the one or more computers toperform the described operations.

Certain implementations of the disclosed systems and techniques may haveparticular advantages. By pairing 2D and 3D path planning, the robot canreduce its computational load while still ensuring that it will identifya collision-free path if one exists. Reducing the computational demandconserves battery power and frees processing capability for other tasks.

By navigating primarily using 2D path planning, a robot can leverage thecomputational efficiency of 2D analysis techniques when navigating openspaces. Additionally, the efficiency of 2D analysis allows high refreshrates and high responsiveness by the robot. Thus, the 2D path planningsystem can perform analysis cycles that more quickly adjust to changingpositions of obstacles and more quickly alter the course of the robot.With higher responsiveness and quicker planning, the robot may be ableto move at a greater speed while dynamically adjusting its path toaccount for obstacles. By switching to 3D path planning when nocollision-free path is identified by the 2D planning, the robot canleverage the accuracy of 3D analysis to navigate through confinedspaces. The 3D analysis also increases the range of options fornavigation, allowing the robot to plan paths that would have appearedblocked by 2D analysis alone.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other potentialfeatures and advantages of the disclosure will be apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a system for local robotnavigation using 2D and 3D path planning.

FIGS. 2A-2B are diagrams illustrating an example of 2D path planning.

FIG. 2C is a diagram illustrating an example of 3D path planning.

FIG. 2D is a diagram illustrating an example of local robot navigationusing 3D path planning.

FIG. 3A is a flow diagram illustrating an example of a process for localrobot navigation using 2D and 3D path planning.

FIG. 3B is a diagram illustrating an example of local robot navigationusing 2D and 3D path planning.

FIG. 4 is a flow diagram illustrating an example of a method for localrobot navigation using 2D and 3D path planning.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 is a diagram illustrating an example of a system 100 for localrobot navigation using 2D and 3D path planning. The system 100 includesa robot 110, which has a local navigation system 130 that includes apath planning module 140 and a navigation module 150. The robot 110 alsoincludes sensors 160. The system 100 also includes a server 170 whichcommunicates with the robot 110 over a network 105. FIG. 1 shows therobot 110 in a room 120 that includes a table 122 and a plant 124. Inthe example, the robot 110 attempts to navigate across the room 120,finding a path between the table 122 and the plant 124.

In general, a robot refers to a machine or apparatus that includes acomputing device and a capability to move through an environment ormanipulate objects in the environment. A robot typically has alocomotion system, such as motorized legs, wheels, or treads, thatenable the robot to travel through its surroundings. The actions andfunctions of the robot can be controlled by one or more programmableembedded or external computing systems, which enable the robot toautonomously or semi-autonomously determine and perform actions,including navigation and locomotion, without input, or with only limitedinput, from a human user.

Many applications require a robot to move through a space to performvarious tasks and actions. Navigating through a space that includesvarious obstacles or objects can be performed using a multistage processinvolving both global and local path planning phases to chart acollision-free path for the robot. In many instances, global and localpath planning represent setting a path using information at differentlevels of specificity. For example, a global path plan may represent ahigh-level description of a route for a robot to travel, e.g., a plan totravel from one room to another through a doorway. Some or all of theglobal path plan may be generated based on data for regions that therobot does not currently observe with its sensors. By contrast, a localpath plan may implement the global plan in a more fine-grained way,e.g., directing specific positions or waypoints within a room or settingcourse corrections to respond to detected obstacles. In this manner, thelocal path plan can implement and dynamically refine the global pathplan as the robot travels, to fit the specific conditions the robotencounters.

In global path planning, the robot determines an initial, preferredglobal path to a desired destination based on information that does notchange frequently, such as the structure of a building. Locations ofpreviously observed objects, such as furniture that is consistently in acertain position, can also be used. The global path planning ofteninvolves setting a general route through areas that the robot does notcurrently observe with its sensors, and so the robot may not be able toverify the specific obstacles along the route. For example, the robotmay use the known floor plan of a building to determine an initial,preferred path from a room on the first floor of the building, through acorridor, up an elevator, and around a corner to a room on the secondfloor of the building. Because the building's layout is known andunchanging, in global path planning, the robot can use this informationto chart a global path that avoids known, static obstacles, such aswalls. The robot can select the preferred global path based on any ofvarious criteria, for instance, a path with the shortest distance, thefastest predicted transit time, or the path least likely to disruptother occupants of the building.

In some implementations, the robot 110 may use 2D floor plan data todetermine the global path. In some examples, the floor plan data ismapped to a 2D occupancy grid, which depicts the position of knownobstacles (e.g., walls, stairwells, elevators, furniture) in the space.A typical global occupancy grid may have grid spacing on the order of0.1 meters to 1 meter, where the grid spaces corresponding to obstaclesor objects are considered occupied and thus impassable by the robot.Typically, the floor plan or the occupancy grid depicts a projection ofobstacles or objects down onto the plane of the floor. As a result, thegrid shows as occupied an area corresponding to the largest span of anobject along the plane of the floor. For example, for a table depictedin an occupancy grid, an area corresponding to the entire table top willbe indicated as occupied and impassable by the robot 110.

The robot 110 determines the preferred global path by charting a routefrom the robot's current location (e.g., the starting point) to itsdestination through the unoccupied regions of the global occupancy grid.For global path planning, to ensure that no part of the robot 110collides with a known obstacle, the robot 110 may use a 2D profile torepresent the robot's overall footprint (e.g., the projection of theshape of the robot 110 onto the plane of the floor) and chart a routealong which the robot's 2D profile does not intersect with any occupiedgrid space.

The robot 110 may periodically update the preferred global path toaccount for deviations in the robot's location from the route. Becausethe information used for global path planning does not changefrequently, the robot's global path may be updated on relatively slowtime scales, e.g. once every few seconds (corresponding to update ratesranging from a fraction of one Hz to one Hz) or in response to a changein a map, floor plan, or occupancy grid. Global path planning can beperformed by a computer system embedded in the robot 110 or by a remotecomputer system, e.g., by a server that periodically sends global pathupdates to the robot 110, or by a combination of local and remoteprocessing.

Because global path planning uses static information such as a floorplan and/or previous observations that may not be current, the selectedglobal path may not account for obstacles that are currently locatedalong the path, but which were not present when the floor plan wasgenerated. For example, the floor plan may not indicate the currentlocation of a chair, or other item of furniture, that has been moved toa location that coincides with the route. The floor plan also isunlikely to indicate the presence of a person who is currently standingalong the route.

To account for the differences between the information used to generatethe global path and the current, actual state of the robot'senvironment, the robot can implement local path planning to modify theglobal route to avoid collisions. In local path planning, the robot usescurrent data from sensors of the robot (e.g., cameras, LIDAR, radar) todetect and locate obstacles along its route. If an obstacle is detectedalong the planned global path and the robot predicts that following theroute could result in a collision with the obstacle, the robot canmodify its path to avoid the collision. In this way, the local pathplanning enables the robot 110 to implement the planned global path,allowing for deviations from the planned global path to fit thesituation encountered by the robot 110.

For example, the robot 110 may divide the global path into several pathsegments. Based on sensor data, if the robot 110 determines that thenext path segment of the preferred global path is blocked by a chair,the robot 110 may replace that path segment with a new path segment thatre-routes the robot 110 around the chair to avoid collision.

Generally, the robot's local path planning attempts to follow the globalpath, modifying the route only if it determines there may be acollision. Because it typically relies on sensing objects in the nearbyenvironment, local path planning is generally performed for pathsegments in the local vicinity of the robot 110 (e.g., those pathsegments within a few meters of the robot 110).

As the robot 110 travels along its path, it performs local path planningfor each subsequent path segment, detecting potential collisions withobstacles and modifying a particular part of the robot's route, ifnecessary, to avoid a collision. Because the robot's nearby surroundingschange as the robot moves, local path planning is typically repeated athigher rates (e.g., on the order of 10 Hz or more), and requires lowerlatencies, than global path updates. As a result, local path planning istypically implemented in a computer system embedded as part of the robot110.

To detect and avoid potential collisions, the robot 110 can use 2D or 3Danalysis techniques. The 2D analysis techniques used for local pathplanning can be similar to the techniques used for global path planning.For example, the robot 110 can generate a local 2D occupancy grid basedon its sensed surroundings and use a 2D profile that represents thefootprint of the robot 110. In some cases, the same occupancy grid usedfor global path planning can be used for local path planning. In othercases, because the robot 110 uses current sensor data to generate orupdate the local occupancy grid, it may have a grid spacing on the orderof a few centimeters, smaller than that of the global occupancy grid.If, when moving the robot 110 profile along the proposed path segment,the profile intersects a blocked space of the local occupancy grid, therobot may identify a potential collision. If the robot 110 identifies apotential collision along a segment of the global path, it can attemptto reroute the robot 110 around the obstacle, but still generally in thedirection of the global path.

While the 2D techniques described above can be computationallyefficient, in some examples, they may fail to identify a collision-freepath, even though one exists. This can occur due to the approximationsused in 2D path planning, especially since the maximum dimensions of anobject are projected onto the occupancy grid. For example, the globalpath may direct the robot 110 to pass between a table and a plant orother object. In the local occupancy grid, the entire area of the tabletop, as well as the largest extent of the plant, would be shown asoccupied. A 2D analysis using a 2D profile of the robot 110 thatrepresents the largest diameter of the robot, may indicate that therobot 110 cannot pass through the space between the table and the plantwithout contacting one or the other. The 2D analysis would thus concludethat there is no collision-free route along the global path. However, ifthe base of the table is significantly narrower than the table top, asis typical, the robot 110 may be able to pass by the table by extendinga portion of it underneath the table top.

When 2D techniques indicate that there may be no collision-free path ina desired direction of travel, the robot 110 can switch to using 3Danalysis techniques for local path planning. The 3D techniques perform amore fine-grained assessment of the feasibility of a path segment bycomparing the 3D shapes of detected objects and the 3D shape of therobot 110 to detect a potential collision. The 3D analysis techniquescan more accurately predict whether a collision will occur, thusidentifying collision-free paths that were excluded by 2D analyses thatdid not account for the full 3D shapes of the robot 110 or nearbyobjects.

In some examples, the 3D analysis techniques can extend local pathplanning further by evaluating the ability of the robot 110 to navigatethrough a confined space in different configurations (e.g., with itsarms elevated, or turned sideways). By evaluating different robotconfigurations, using 3D analysis techniques, the robot 110 maydetermine that the robot 110 can pass through a confined space in aparticular configuration, allowing the robot 110 to navigate a routethat the 2D techniques indicated was blocked.

Though 3D analysis techniques are more accurate, they may requireconsiderably more computing resources than 2D analysis techniques. As aresult, in some examples, the robot 110 implements 2D analysistechniques generally, switching only to 3D analyses when the 2Dtechniques fail to identify a collision-free route. Then, after the 3Danalyses complete (e.g., after a collision-free path is identified), therobot 110 can switch back to 2D techniques for computationallyefficiency.

Local navigation includes local path planning, as described above, aswell as motor command generation and further fine-grained collisionavoidance analyses. To perform local navigation, the robot 110 includesa local navigation system 130, which implements a path planning module140 and a navigation module 150. The path planning module 140 performslocal path planning to determine collision-free path segments for therobot 110 using 2D and 3D analyses. Once the planning module 140 hasdetermined a path, the navigation module 150 issues motor commands andprovides local kinematic awareness and modeling for fine-grainedcollision avoidance. By switching between 2D and 3D analysis for localpath planning, the robot 110 can ensure that existing collision-freepaths are identified, while efficiently managing computing resources andload.

Referring still to FIG. 1 , the robot 110 includes computing hardware aswell as a locomotion system, such as motorized legs, wheels, or treads,enabling it to travel through a space. The actions and functions of therobot 110 are controlled by one or more embedded computer systems, whichenable it to autonomously, or semi-autonomously, determine and performactions, including navigation and locomotion. In the example of FIG. 1 ,the robot 110 is located in the room 120, which contains the table 122and the plant 124 positioned such that there is a narrow space betweenthem.

In some implementations, the robot 110 communicates with a server 170.The server 170 can be, for example, one or more computers, computersystems, server systems, or other computing devices. In someimplementations, the server 170 is a cloud computing platform.

In FIG. 1 , the robot 110 communicates with the server 170 through anetwork 105, which can include any combination of wired or wirelesscommunication links. For example, the network 105 can include a localarea network (LAN), a wide area network (WAN), Ethernet, a cellular datanetwork, a Wi-Fi network, the Internet, or any other network topologythat enables the exchange of data.

In system 100, the server 170 performs global path planning for therobot 110, generating a preferred global path for the robot based on acurrent task for the robot 110. In the example of FIG. 1 , the robot'stask is to open a window on the back wall of the room 120. Based on therobot's task, the server 170 determines a global path 126, whichrequires the robot 110 to travel from its current position at the frontof the room 120 to the back wall of the room 120 by passing in betweenthe table 122 and the plant 124. At regular intervals (e.g., at anapproximately one Hz rate) or as needed, the server 170 updates theglobal path 126 based on the robot's current location and provides tothe robot 110 the updated global path data 172, which describes thedesired path 126 for the robot 110 to travel.

Based on the received global path data 172, the robot 110 navigatesthrough the room 120 using its local navigation system 130. The localnavigation system 130 performs various operations related to local pathplanning and locomotion that enable the robot 110 to move about the room120 without colliding with other objects in the space.

The local navigation system 130 includes a path planning module 140 anda navigation module 150. The planning module 140 performs local pathplanning for the robot 110, determining collision-free path segmentsgenerally along the global path. After one or more collision-free pathsegments have been determined, the navigation module 150 generates motorcommands 152 to produce robot motion and performs fine-grained kinematicmodeling and sensing to ensure that the robot 110 avoids collisions whenexecuting the motor commands 152.

The navigation system 130 is typically embedded as part of the robot110, for example, as part of a computer system of the robot 110. Thenavigation system 130 can be implemented in any combination of software,firmware, and hardware, including one or more computer programs, generalor special purpose central processing units (CPUs), field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs) orother computational methods, systems, or devices.

The path planning module 140 includes a 2D planner 142, a 3D planner144, and a mode select module 146. The mode select module 146 selectswhether local path planning will be accomplished using 2D or 3D analysistechniques (e.g., by the 2D planner 142 or the 3D planner 144,respectively). In some examples, to conserve computing resources, themode select module 146 selects the 2D planner 142 by default.

To determine the location of obstacles in the robot's local environment,the planning module 140 uses sensor data 162 received from sensors 160of the robot 110. For example, the robot 110 may include various sensors160, such as one or more cameras, radar systems, LIDAR, proximitysensors, or other detectors. The one or more cameras 160 can includemonovision systems, as well as RGB-D devices, stereo cameras, or othersystems that provide depth information. The sensors 160 generate data162 that provides information about objects in the robot's localenvironment (e.g., within a few meters of the robot). The sensor data162 can include raw or processed image data, video data, LIDAR signals,radar returns, proximity data, or other sensor data that providesinformation on the presence, location, size, or shape of obstacles nearthe robot 110.

Based on the received sensor data 162, the 2D planner 142 generates alocal 2D occupancy grid that indicates the current location of obstaclesor objects in the robot's local environment. The local occupancy gridmay be similar to the global occupancy grid used for global pathplanning, but may have a smaller grid spacing (e.g., a local gridspacing of a few centimeters compared to a global grid spacing of 0.1 to1 meter), enabling the robot 110 to determine more accurately thecurrent occupancy of the space. Similar to the global occupancy grid,the local occupancy grid typically depicts a projection of an object orobstacle onto the plane of the floor. As a result, the local occupancygrid shows as occupied an area corresponding to the largest span of anobject (e.g., for a table, the occupied area of the grid corresponds tothe area of the table top).

In some examples, the planning module 140 may also use 2D floor plandata 174 to determine the location of objects in the robot's localenvironment. The floor plan data 174 can be provided by the server 170and may be the same data used by the server 170 for global pathplanning. In some examples, the floor plan data 174 may be stored in amemory system of the robot 110. For example, the planning module 140 mayuse sensor data 162 to update or modify the floor plan data 174 togenerate a local occupancy grid that reflects the current location ofobstacles in the robot's local environment.

The planning module 140 also receives the global path data 172describing the preferred path for the robot 110 to travel. In someexamples, the global path data 172 is generated by the robot 110. In theexample of FIG. 1 , the global path data 172 is provided to the robot110 by the server 170 and indicates a path 126 directing the robot 110to move from the front to the back of the room 120 by traveling betweenthe table 122 and the plant 124.

The planning module 140 also accesses robot data 136. The robot data 136includes information describing the physical shape and size of the robot110. For example, the robot data 136 may include one or more 2D profilesof the robot 110. The 2D profiles can be, for example, the outerdimensions of the robot 110 in one or more horizontal planes that areparallel to the floor (e.g., horizontal cross-sections of the robot110). In some examples the robot data 136 include a maximum 2D profileand a minimum 2D profile. The maximum 2D profile can represent a maximumdimension, circumference, or span of the robot 110 that must beaccommodated for the robot 110 to maneuver through a particular space.For example, the maximum profile may be a projection of the outerdimensions of the robot 110 onto a plane parallel to the floor thatindicates the largest footprint of the robot 110, e.g., reflecting themaximum dimensions of the robot 110 in any horizontal plane along theheight of the robot 110. In the example of FIG. 1 , the maximum profilecorresponds to a cross-section of the robot 110 across its base 111. Themaximum profile may represent a maximum dimension across all potentialposes or configurations of the robot 110, for example, showing the widthof the arms when the arms are at their maximum outward extension.Alternatively, the maximum profile may represent the maximum dimensionsfor the current pose or configuration of the robot, e.g., based on theactual current position of the arms. If the robot 110 is carrying anobject that extends away from the robot 110, the extent of the objectcan also be included in the current maximum profile of the robot 110 toavoid collision with the carried object.

Likewise, the minimum 2D profile can represent a minimum dimension,circumference, or span of the robot 110 that must be accommodated forthe robot 110 to maneuver through a particular space. For example, theminimum profile can correspond to the horizontal cross-section of therobot 110 that has the smallest area or smallest dimensions. In theexample of FIG. 1 , the minimum 2D profile corresponds to across-section of the robot 110 across its trunk 112.

The robot data 136 also includes data describing the 3D shape of therobot. For example, the robot data 136 can include a 3D model of therobot, 2D profiles or cross-sections in various orientations (e.g.,parallel and perpendicular to the floor), a listing of the robot'sdimensions, or other data describing the robot's shape, size, and spanin three dimensions. In some examples, the robot data 136 includes 2D or3D shape data related to different configurations of the robot 110, forexample, data describing the shape and span of the robot 110 when itsarms are elevated or when it is configured in a different orientation(e.g., turned sideways).

Using the generated local occupancy grid and the robot data 136, the 2Dplanner 142 attempts to determine a series of collision-free pathsegments for the robot 110 to travel along the global path 126. The 2Dplanner 142 can predict a potential collision by tracing a maximum 2Dprofile of the robot along the route described by the global path data172. The 2D planner 142 detects a potential collision at a location atwhich any portion of the maximum 2D profile intersects an occupied spaceof the local occupancy grid.

For example, based on the global path data 172, the 2D planner 142 candivide the portion of the global path near the robot's current locationinto shorter path segments. Starting at the current location of therobot 110, the 2D planner 142 can evaluate whether there would be apotential collision if the robot 110 moved along the next path segment.If the 2D planner 142 determines there are no potential collisions alongthe next path segment, it can evaluate a subsequent path segment forcollision. The 2D planner 142 can continue to evaluate subsequent pathsegments until a predetermined series of segments are determined to becollision-free. After the 2D planner 142 identifies a series of segmentsthat are collision-free, the planner 142 generates path data 148describing the identified route.

If the 2D planner 142 determines that a path segment may result in apotential collision, it can attempt to determine an alternate,collision-free route that moves the robot 110 generally along the globalpath while avoiding the obstacle. For example, the 2D planner 142 maydetermine that the next path segment of the global path 126 may resultin the robot 110 colliding with the table 122. The 2D planner 142 canthen attempt to determine a path that navigates the robot 110 around thetable 122 while avoiding any other obstacle in the room 120.

To determine an alternate, collision-free route, the 2D planner 142 cangenerate and evaluate multiple candidate path segments that move therobot 110 generally along the global path 126. For instance, the 2Dplanner 142 can generate candidate segments that deviate from the globalpath 126 by a particular angle (e.g., 10 degrees, 20 degrees, 30degrees, etc.) For each candidate segment, the 2D planner 142 evaluateswhether there would be a potential collision if the robot 110 movedalong that segment. For any segment that the 2D planner 142 determinesto be collision-free, the planner 142 can repeat the process, generatingnew candidate segments that start at the end point of the first segmentand extend further in the general direction of the global path. The 2Dplanner 142 then evaluates the new candidate segments to identify asegment that is collision-free. The 2D planner 142 can repeat thisprocess until it determines one or more collision-free paths aroundobstacles detected in the robot's local environment.

In some implementations, the generation of the collision-free routeresembles a tree structure, where the 2D planner 142 prunes those routesor segments that result in a potential collision. If more than onecollision-free route is identified, the 2D planner 142 can select aparticular collision-free path based on any of various criteria (e.g.,shortest distance, least deviation from the global path, most clearancefrom any detected obstacle or object). After the 2D planner 142 hasselected a collision-free path, it generates path data 148 describingthe selected path.

In some examples, the 2D planner 142 may fail to identify acollision-free route along the global path 126 (e.g., if a person orobject has moved to complete block the path). However, in otherexamples, the 2D planner 142 may fail to identify a collision-free routeeven if a collision-free path does exist (e.g., in thepreviously-described scenario in which the robot can pass by the tableonly if a portion of the robot extends underneath the table top). If the2D planner 142 failed to identify an existing collision-free path, themode select module 146 can switch to the 3D planner 144, to implement 3Danalysis techniques to identify the collision-free path.

Because 3D analysis techniques can be computationally expensive, theplanning module 140 may prefer to implement them only if it determinesthat a collision-free path likely does exist. The planning module 140can check whether a potential collision-free path exists by performing2D analysis techniques using a minimum 2D profile of the robot. Similarto the analyses using the maximum profile, the 2D planner 142 canidentify a potential collision-free path by searching for routes alongwhich the minimum profile reaches the destination without intersectingany occupied grid spaces of the local occupancy grid.

Because the minimum 2D profile is usually smaller than the maximum 2Dprofile, the 2D planner 142 may identify a path that is collision-freewhen using the minimum profile, but which was not collision-free usingthe maximum profile (e.g., if the space between obstacles is smallerthan the maximum profile, but larger than the minimum profile). If the2D planner 142 identifies a collision-free path segment using theminimum profile, the mode select module 146 switches to the 3D planner144. In some examples, the mode select module 146 may require the 2Dplanner 142 may to determine that multiple subsequent candidate pathsegments are collision-free using the minimum profile before it switchesto the 3D planner.

The 3D planner 144 performs a more fine-grained analysis using 3Dtechniques to identify a collision-free route for the robot 110 that isgenerally along the global path 126. The 3D techniques implemented bythe 3D planner 144 account for the 3D shape of the robot 110 (e.g.,accounting for the differing dimensions of the robot 110 along itsheight), as well as the 3D shapes of the obstacles detected in therobot's local environment to more accurately evaluate the potential forcollision along a candidate path segment.

To perform 3D analyses, the 3D planner 144 can generate a 3D profile ofthe robot 110 based on the robot data 136. The 3D robot profile canspecify, for example, the span or circumference of the robot 110 atmultiple, different heights from the floor or identify a 3D bounding boxfor the robot 110. The 3D profile can be based on a CAD or other modelof the robot. In some implementations, the 3D planner 144 may generate a3D profile that accounts for movable components of the robot 110, forinstance, by generating a 3D profile that can be modified to reflect aparticular configuration the robot 110 (e.g., with its arms elevated,turned sideways).

Based on the sensor data 162, the 3D planner 144 can also generate 3Dprofiles for one or more objects near the robot 110 in the direction ofthe candidate segment. Similar to the 3D robot profile, the 3D profilesof the objects can specify a span or circumference of the object atmultiple different heights from the floor or identify a 3D bounding boxfor the object. In some examples, the 3D planner 144 may generate apoint cloud representing the external surface of the one or moreobjects. In some examples, the 3D planner 144 may generate the 3Dprofile of the object based, at least in part, on data related to thetype of object. For example, the robot 110 may identify a particulardetected object as a table and use one or more CAD models of a tablefrom a database to generate a 3D profile for an object.

The 3D planner 144 can then analyze the 3D profile of the robot 110relative to the 3D profiles of any detected obstacles to determinewhether movement of the robot 110 along the candidate path segment wouldlead to a potential collision with a nearby object. A potentialcollision would be indicated, for example, if a portion of the 3Dprofile of the robot 110 intersected a portion of the 3D profile of theobject at any location along the segment. If the 3D planner 144determines that the robot 110 can safely travel along the path segmentwithout colliding with an obstacle, the planning module 140 can selectthe path segment, the mode select module 146 can switch back to the 2Dplanner 142, and the planning module 140 can plan the next path segmentusing 2D analysis techniques.

In some examples, the 3D planner 144 may repeat the analysis using 3Dprofiles that correspond to different configurations of the robot 110(e.g., with robot arms elevated, with robot body turned sideways). Forexample, if the 3D planner 144 determines that the robot 110 cannot passby a detected object in its typical configuration (e.g., arms by itsside), the planner 144 may repeat the 3D analysis using a 3D shapecorresponding to a different configuration of the robot. In someexamples, the 3D planner 144 repeats the 3D analyses using 3D profilescorresponding to different robot configurations until a collision-freeconfiguration is identified or until a predetermined set ofconfigurations has been analyzed.

In some implementations, the planning module 140 may plan more than onesequential segment using 3D analysis techniques. In these examples, the3D planner 144 can identify a collision-free path using a method similarto that implemented by the 2D planner 142, but using 3D analysistechniques to detect potential collisions. In brief, the 3D planner 144can divide the local portion of the global path into shorter pathsegments then generate a candidate segment that generally moves therobot 110 along the global path 126. Using the 3D analysis techniquesdescribed above, the 3D planner 144 then determines whether moving therobot 110 along that segment would result in a potential collision. Ifthe 3D planner 144 determines that a particular candidate segment wouldresult in a potential collision, the 3D planner 144 evaluates adifferent candidate segment, perhaps varying the direction of thesegment. The 3D planner 144 can also evaluate the same segment but witha different robot configuration (e.g., with arms elevated) ororientation (e.g., turned sideways). When the 3D planner 144 identifiesa candidate segment and robot orientation that is collision-free, the 3Dplanner 142 can generate and evaluate a subsequent candidate segmentthat starts at the end point of the first segment.

Like the 2D planner, the 3D planner 144 can repeat this process until itdetermines one or more collision-free paths around one or more obstaclesin the robot's local environment. If multiple collision-free paths areidentified, the 3D planner 144 can select one path based on any ofvarious criteria (e.g., shortest distance, least deviation from theglobal path, most clearance from any detected obstacle or object, mostpreferred robot configuration).

In some examples, neither the 2D planner 142, using maximum or minimumprofiles) nor the 3D planner 144, may identify a collision-free pathalong the global path 126. In these examples, the planning module 140may determine that no collision-free path exists.

After a collision-free path is identified and selected, the module 140generates path data 148 describing the selected route. If the 3D planner144 was used to select any segment of the path, the module 140 may alsogenerate configuration data 149 describing the particular configurationof the robot 110 required to execute the path without collision (e.g.,the robot 110 must elevate its arms for a portion of the path).

The planning module 140 provides the path data 148 and configurationdata 149 to the navigation module 150. The navigation module 150 thengenerates motor commands 152 that are sent to the robot's motor control180 to produce the locomotive forces necessary to move the robot 110along the path described by the path data 148 and in the configurationdescribed by the configuration data. The navigation module 150 also canuse the sensor data 162 and other data to perform kinematic modeling ornear-real-time collision avoidance to ensure that, as the robot 110executes the motor commands 152 to follow the determined path, it doesnot collide with any object or person.

FIGS. 2A-2D are diagrams illustrating an example of local robotnavigation using 2D and 3D path planning for the robot 110 of FIG. 1 .In FIGS. 2A-2D, the robot 110 receives data indicating a preferredglobal path 126 that directs the robot 110 to move from the front of theroom to the back of the room by traveling between the table 122 and theplant 124. While the gap between the top of the table 122 and the plant124 is narrower than the robot's base, the robot 110 can travel throughthe gap without colliding with either the table 122 or the plant 124 byextending its robot base underneath of the table top and elevating itsrobot arms above the table top.

FIG. 2A is a diagram illustrating an example 200 a of 2D path planningusing a maximum 2D robot profile. In FIG. 2A, based on sensor datareceived from sensors of the robot 110, the 2D planner generates a local2D occupancy grid. The local occupancy grid includes areas 222 and 224that are denoted as “occupied” by the table 122 and the plant 124,respectively.

Based on the robot data, the 2D planner generates a maximum 2D profile232 a of the robot 110. The maximum profile 232 a corresponds to thewidest span of the robot 110 that would pass through the space betweenthe table 122 and the plan 124, e.g., the circumference of the base ofthe robot 110 in this example.

The 2D planner also receives global path data describing the preferredpath 126, which directs the robot 110 to travel between the table 122and the plan 124. Because the diameter of the maximum profile 232 a islarger than the space between the areas 222 and 224 occupied by thetable 122 and the plant 124, respectively, the 2D planner determinesthat there is no collision-free path. Without 3D path planning, therobot's attempt to locally navigate the preferred path 126 may end here,with a determination that there is no collision-free path, even thoughone does exist. Because the robot 110 also implements 3D path planning,it can switch to using 3D analysis techniques to identify thecollision-free path.

To determine whether the robot 110 should switch to using 3D analysistechniques, the path planning module of the local navigation systemfirst evaluates whether a collision-free path potentially exists using aminimum 2D profile. If the module does not identify a collision-freepath using a minimum 2D profile, the system determines that there is nofeasible collision-free path along the global path. However, if themodule identifies a potential collision-free path using a minimumprofile, the system then switches to using 3D analysis techniques.

In example 200 b of FIG. 2B, the robot 110 performs 2D path planningusing a minimum 2D profile 232 b. The 2D planner of the robot generatesthe minimum profile 232 b of the robot 110 based on accessed robot data.In this example, the minimum profile 232 b corresponds to the narrowestspan of the robot 110 that would pass through the space between thetable 122 and the plant 124 (e.g., the circumference of the robot'strunk).

The 2D planner evaluates the desired global path 126 using the minimumprofile 232 b and determines that, because the minimum profile 232 b issmaller than the gap between the table 122 and the plant 224, there ispotentially a collision-free route along the global path 126.

Because the 2D planner determined that a potential collision-free pathexists using the minimum profile 232 b, the robot's path planning moduleswitches to using 3D analysis techniques (e.g., the 3D planner) toidentify the collision-free path. FIG. 2C is a diagram illustrating anexample 200 c of 3D path planning. In example 200 c, the 3D planner ofthe robot generates a 3D profile 232 c of the robot 110 based onaccessed robot data. The 3D profile 232 c includes a bounding boxindicating the outer extent of the robot 110 in three dimensions. In theexample of FIG. 2C, the bounding box does not include the robot's arms,as they are movable and can be adjusted as required.

The 3D planner also generates 3D profiles 222 c and 224 c of the table122 and the plant 124, respectively. For example, the 3D planner cangenerate the profiles 222 c and 224 c based on image and depth datareceived from one or more RGB-D cameras of the robot 110.

Based on analyzing the 3D robot profile 232 c, as well as the 3Dprofiles 222 c and 224 c of the table 122 and the plant 124,respectively, the 3D planner determines that the robot 110 can travelalong the path between the table 122 and plant 124 without collidingwith either if the robot 110 elevates its arms.

Based on the 2D and 3D path planning analyses, the robot's path planningmodule can select the collision-free path, and send data describing thatpath, as well as data indicating the required robot configuration, tothe robot's navigation module.

FIG. 2D is a diagram illustrating an example 200 d of local robotnavigation using the 2D and 3D path planning of FIGS. 2A-2C. In example200 d, based on the selected path and robot configuration (i.e., robotarms elevated) determined by the path planning module, the robot 110successfully navigates the path between the table 122 and the plant 124.

FIG. 3A is a flow diagram illustrating an example of a process 300 a forlocal robot navigation using 2D and 3D path planning. In process 300 a,the robot's local navigation system first attempts to determine acollision-free route along the preferred global path using 2D analyseswith a maximum 2D profile of the robot. If the system cannot identify acollision-free path, it then determines whether to perform 3D pathanalyses by first evaluating whether any potential collision-free pathexists using 2D techniques with a minimum 2D profile of the robot. Ifthe system identifies a collision-free path using the minimum profile,the system then performs 3D analyses to identify the collision-freepath. If the system identifies the collision-free path using 3Danalyses, it selects the identified path segment and sends datadescribing the path segment and robot configuration to the robot's motorcontrol.

In process 300 a, based on the preferred global path, the localnavigation system's 2D planner first generates candidate path segmentsalong the preferred path for the robot (302). For example, starting atthe current location of the robot, the 2D planner may generate multiplecandidate path segments that extend generally in the direction of theglobal path, but vary in angle relative to the path (e.g., vary byangular steps of 15 degrees). The lengths of the candidate path segmentsmay be the same or may vary, but typically are short enough to remainwithin a nearby range of the robot (e.g., within a few meters).

The 2D planner then evaluates the candidate path segments using amaximum 2D profile of the robot to determine whether each segmentresults in a potential collision (304). The 2D planner can generate themaximum profile based on stored robot data that indicates the robot'sphysical size, shape, or dimensions. In some implementations, the 2Dplanner generates a local 2D occupancy grid based on sensor data thatindicates the location of objects in the robot's local vicinity. The 2Dplanner detects a potential collision along a given candidate pathsegment if moving the robot along that segment would cause the maximumprofile to intersect with an occupied space of the local occupancy grid.

Using the maximum profile and the local occupancy grid, the 2D plannerevaluates the candidate path segments to determine whether any of thecandidate path segments result in a collision-free path (306). If the 2Dplanner determines that one or more of the candidate path segments arecollision-free, the 2D planner can select a path segment (308) fromamong the collision-free segments then repeat the analysis (returning tostep 302) to determine a subsequent path segment.

If the 2D planner determines that none of the candidate path segmentsare collision-free using the maximum profile, the system then determineswhether to switch to 3D analyses by first evaluating the candidate pathsegments using a minimum 2D profile of the robot (310). For example, the2D planner can generate the minimum profile of the robot based on storedrobot data. The 2D planner can then use the generated local occupancygrid to determine whether a potential collision may occur were the robotto move along a given candidate path segment. Similar to the 2D analysesusing the maximum profile, a potential collision is detected if movingthe robot along the segment would cause the minimum profile to intersectwith an occupied space of the local occupancy grid. In someimplementations, the 2D planner may identify new candidate path segments(e.g., candidate path segments different from those evaluated using themaximum profile) to evaluate using the minimum 2D profile.

The 2D planner evaluates the candidate path segments to determinewhether there is a collision-free path using the minimum 2D profile(312). If the 2D planner determines that none of the candidate pathsegments result in a collision-free route, the planner determines thatthere is no collision-free route along the preferred global path (314).In this case, the navigation system may return a message to the globalpath planning system (e.g., to a remote server) indicating that thepreferred global path is not navigable. In some examples, the 2D plannermay attempt to determine an alternate route, for instance, by generatingand evaluating alternate candidate path segments that diverge more fromthe global path.

However, if the 2D planner determines that a particular candidate pathsegment is collision-free when evaluated using the minimum profile, thelocal navigation system switches to 3D path analyses. Using 3D analysistechniques, the 3D planner evaluates the particular candidate pathsegment to determine whether the segment is, in fact, collision-free(316). For example, as described previously, based on sensor datareceived from sensors of the robot (e.g., cameras, LIDAR, radar, etc.),the 3D planner can generate 3D profiles of objects detected in therobot's local environment. The 3D planner also generates a 3D profile ofthe robot in one or more configuration (e.g., with arms elevated, turnedsideways). Based on the 3D profiles of the detected objects and therobot, the 3D planner determines whether the robot can travel along theparticular candidate path segment without colliding with a detectedobject.

If the 3D planner determines that the particular candidate path segmentis collision-free (318), it selects the path segment (308) and thencontinues planning the route by returning to step 302 to perform 2D pathplanning of the next path segment (e.g., a path segment that starts atthe previous segments end point).

In some implementations, if the 3D planner determines that theparticular candidate path segment results in a potential collision, the3D planner may repeat step 316 for various robot configurations until acollision-free path is detected or until all of a predetermined set ofconfigurations have been analyzed.

If the 3D planner determines that the robot cannot move along thecandidate path segment without potentially colliding with an object, theplanner determines that there is no collision-free route along theglobal path (314). As above, in this case, the navigation system mayreturn a message to the global path planning system (e.g., a remoteserver) indicating that the preferred global path is not navigable oruse local path planning to identify an alternate route that divergesmore from the global path.

As noted above, once a collision-free candidate path segment has beenselected (308), the local navigation system can continue path planningby repeating steps 302 through 318 as necessary. After a predeterminednumber of path segments have been selected, the system generates pathdata 348 describing the selected path and robot configuration data 349describing the required robot configuration. The system provides thepath data 348 and configuration data 349 to a navigation module 350,which generates motor commands 352 that are sent to the robot's motorcontrol 380 to cause the robot to travel along the identifiedcollision-free path.

FIG. 3B is a diagram illustrating an example 300 b of local robotnavigation using the 2D and 3D path planning process 300 a of FIG. 3Afor a robot navigating along a preferred global path between a table andplant in a room, as in FIGS. 1 and 2A-2D.

In FIG. 3B the local navigation system of the robot has alreadydetermined a collision-free path segment from the starting point “0” tointermediate point “1” using 2D path planning with a maximum 2D profile.To determine the next path segment, in step 302′ the 2D plannergenerates candidate path segments that would move the robot generallyalong the preferred global path. In example 300 b, the 2D plannergenerates three candidate path segments, extending from point “1” topoints “2a,” “2b,” and “2c,” respectively, as shown in panel 320. Thepanel 320 also shows the locations of the table and plan, where areas322 and 324 are designated as occupied in the robot's local 2D occupancygrid.

In step 304′, the 2D planner uses a maximum 2D profile 332 a of therobot to evaluate whether any of the candidate path segments arecollision-free. Because the maximum profile 332 a intersects with anoccupied space of the local occupancy grid for all three of thecandidate path segments, the 2D planner determines that none of thecandidate path segments are collision-free path when evaluated using themaximum profile 332 a (306′).

Because the 2D planner did not identify a collision-free path using themaximum profile (306′), the 2D planner next determines whether apotential collision-free path exists by evaluating the candidate pathsegments using a minimum 2D profile 332 b (310′). As shown in panel 330,the 2D planner evaluates each of the three candidate paths using thelocal 2D occupancy grid and determines that candidate segment from point“1” to point “2b” is collision-free when evaluated using the minimumprofile 332 b (312′).

Because the 2D planner determines that there is a collision free pathsegment with the minimum 2D profile 332 b, the local navigation systemswitches to using 3D analysis techniques to identify the collision-freepath (316′). Based on sensor data, the 3D planner generates 3D profilesof the objects in the robot's local environment. In example 300 b, the3D planner generates 3D profiles 322 c and 324 c for the table and theplant, respectively, as shown in panel 340. Based on stored robot data,the 3D planner generates a 3D robot profile 332 c corresponding to the3D shape of the robot.

Using 3D analyses, the 3D planner evaluates the candidate path segmentto determine whether there is a robot configuration that results in acollision-free route along the path segment (316′). In example 300 b,the 3D planner determines that the robot can pass between the table andthe plant without collision along the segment from point “1” to point“2b” if the robot elevates its arms (318′).

Because the 3D planner identified a collision-free path using the 3Danalyses (318′), the navigation system selects the identified pathsegment (from point “1” to point “2b”) and continues path planning byreverting to 2D analyses using the maximum profile (302″). The 2Dplanner repeats step 302, generating three new candidate path segmentsfrom point “2b” to points “3a,” “3b”, and “3c,” respectively. The 2Dplanner then evaluates each of the new candidate path segments using themaximum 2D profile 332 a to determine whether a collision-free route canbe identified (304″). As shown in panel 350, the 2D planner determinesthat segment “2b-3b” is collision-free using the maximum 2D profile. Asa result, the system selects the path segment “2b-3b” then continuescharting the robot course, according to process 300 a, with the nextpath segment starting at point “3b.”

Panel 360 of FIG. 3B shows the final path selected by the system, wherethe solid portions of the line indicates that the starting and endingsegments of the path were determined using 2D path planning, while thedashed portion of the line indicates that the middle segment of thepath, between the table and the plan, was determined using 3D pathplanning.

FIG. 4 is a flow diagram illustrating an example of a method 400 forlocal robot navigation using 2D and 3D path planning. The method 400 canbe performed by one or more computer systems, for example, a computersystem of the robot 110. Briefly, the method 400 includes accessing, bythe robot, map data indicating a 2D layout of objects in a space (402);evaluating candidate paths for the robot to traverse the space based onthe map data (404); determining that the candidate paths do not includea collision-free path (406); in response to the determination,evaluating a 3D shape of the robot with respect to a 3D shape of anobject in the space (408); and determining a path based on evaluatingthe 3D shape of the robot with respect to the three-dimensional shape ofan object in the space (410).

In more detail, the method 400 includes accessing, by the robot, mapdata indicating a 2D layout of objects in a space (402). The map datacan include, for example, a floor plan indicating the location ofvarious objects and/or obstacles, including walls, barriers, barricades,furniture, or other objects that may impede the robot's movement. Insome implementations, the map data can include one or more occupancygrids, where those grid spaces that correspond to objects or obstaclesin the space are indicated. In some implementations, the robot receivesthe map data from a remote computer system, for instance, from a serverover a communication network. In some implementation, the robot accessthe map data by retrieving the map data from a memory system of therobot.

The method also includes evaluating, by the robot, candidate paths forthe robot to traverse the space based on the map data (404). Forexample, the robot may access a global path indicating a generaldirection or destination of the robot. Based on the map data, sensordata of the robot, or other data, the robot may determine a location ofan object in the space and a 2D area occupied by the object. The robotmay then generate a path segment for the robot to traverse, where thepath segment is generally directed along the global path and the pathsegment does not intersect the area occupied by the object. In someimplementations, the robot may generate more than one candidate pathsegments generally directed along the global path.

The method further includes determining, by the robot, that thecandidate paths do not include a collision-free path across the spacefor a 2D profile of the robot (406). For example, the robot may accessdata indicating a 2D profile of the robot, where the 2D profileindicates a 2D shape of the robot. In some implementations, the 2Dprofile may be a minimum 2D profile of the robot, for example, a minimumspan or circumference of the robot in a particular plane. The robot cangenerate a 2D area along a candidate path segment by tracing the 2Dprofile of the robot along a length of the segment. The robot canfurther determine that the candidate path segment is not collision-freeby determining that the area along the candidate segment intersects anarea occupied by an object. If none of the generated candidate pathsegments include a collision-free path, the robot can determine that thecandidate paths do not include a collision-free path across the spacefor the particular 2D profile of the robot considered.

In response to determining that the candidate paths do not include acollision-free path across the space for the 2D profile of the robot,the robot can evaluate a 3D shape of the robot with respect to a 3Dshape of an object in the space (408). For example, the robot may accessdata indicating a 3D shape of the robot, where the 3D shape can includespans or circumferences of the robot at multiple, different heights fromthe floor. In some cases, the 3D shape can indicate a 3D bounding boxfor the robot, or can be based on a 3D model of the robot (e.g., a CADmodel). The 3D shape may correspond to a particular robot configuration(e.g., with its arms elevated, turned sideways). Similarly, the 3D shapeof the object can include spans or circumferences of the object atmultiple, different heights from the floor, a 3D bounding box for theobject, or be based on a 3D model of the object. In some cases, therobot may determine the 3D shape of an object based on data from one ormore sensors of the robot, for example, data from one or more cameras,LIDAR sensors, radar sensors, or other robot sensors. The robot canevaluate the 3D shape of the robot with respect to the 3D shape of theobject to determine whether a generated path segment is collision-free.

The method also includes determining, by the robot, a path based onevaluating the 3D shape of the robot with respect to the 3D shape of anobject in the space (410). For example, if, based on evaluating the 3Dshape of the robot with respect to the 3D shape of one or more objectsin the space, the robot determines that a particular candidate path iscollision-free, it can select that candidate path for the robot totraverse.

In some implementations, the 3D shape of the robot may correspond to aparticular configuration of the robot (e.g., with the robot arms to thesides). Based on evaluating the 3D shape corresponding to the particularrobot configuration with respect to the 3D shape of an object in thespace, the robot may determine that the candidate path would result in acollision between the robot and the object (e.g., the 3D shape of therobot intersects the 3D shape of the object at some point along thecandidate path). The robot may then repeat the 3D analysis using asecond 3D shape of the robot, for example, a 3D shape that correspondsto a second, different robot configuration (e.g., with the robot armselevated) to determine whether a collision-free path exists. Based onevaluating the second 3D shape of the robot with respect to the 3D shapeof the object, the robot may be able to determine a collision-free pathfor the robot.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The techniques can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The machine-readable storage device may be a non-transitorymachine-readable storage device. The described features can beimplemented advantageously in one or more computer programs that areexecutable on a programmable system including at least one programmableprocessor coupled to receive data and instructions from, and to transmitdata and instructions to, a data storage system, at least one inputdevice, and at least one output device. A computer program is a set ofinstructions that can be used, directly or indirectly, in a computer toperform a certain activity or bring about a certain result. A computerprogram can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theelements of a computer are a processor for executing instructions andone or more memories for storing instructions and data. Generally, acomputer will also include, or be operatively coupled to communicatewith, one or more mass storage devices for storing data files; suchdevices include magnetic disks, such as internal hard disks andremovable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a touchscreen and/or a keyboard and a pointing device suchas a mouse or a trackball by which the user can provide input to thecomputer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, a socialnetworking platform, rather than an external application, may performemployment analysis and provide employment information to users of thesocial networking platform. Accordingly, other implementations arewithin the scope of the following claims.

What is claimed is:
 1. A method performed by one or more computers, themethod comprising: accessing data indicating a layout of objects in aspace; determining that a candidate path for a robot to traverse thespace may result in a potential collision; in response to thedetermination, evaluating shapes of the robot that correspond todifferent poses or configurations of the robot; based on the evaluation,identifying one of the poses or configurations of the robot that enablesthe robot to traverse the space without a collision; and providing datathat instructs or causes the robot to traverse the space in theidentified pose or configuration.
 2. The method of claim 1, whereindetermining that the candidate path may result in a potential collisionis based on a maximum profile of the robot that represents a maximumdimension across multiple potential poses or configurations of therobot.
 3. The method of claim 1, wherein evaluating shapes of the robotthat correspond to different poses or configurations of the robotcomprises comparing a shape of a region between obstacles withthree-dimensional shapes of the robot that correspond to different posesor configurations of the robot.
 4. The method of claim 1, wherein therobot has an arm, and wherein the different poses or configurations ofthe robot comprises different positions of the arm with respect to therest of the robot.
 5. The method of claim 1, wherein determining thatthe candidate path may result in a potential collision is based on atwo-dimensional profile for the robot.
 6. The method of claim 1, whereinthe shapes of the robot that correspond to different poses orconfigurations of the robot include three-dimensional shapes forapproaches by the robot in which different sides or portions of therobot face in the direction of travel of the robot.
 7. The method ofclaim 1, wherein accessing data indicating the layout comprisesreceiving map data from a server over a communication network.
 8. Asystem comprising: one or more computers; and one or morecomputer-readable media storing instructions that are operable, whenexecuted by the one or more computers, cause the one or more computersto perform operations comprising: accessing data indicating a layout ofobjects in a space; determining that a candidate path for a robot totraverse the space may result in a potential collision; in response tothe determination, evaluating shapes of the robot that correspond todifferent poses or configurations of the robot; based on the evaluation,identifying one of the poses or configurations of the robot that enablesthe robot to traverse the space without a collision; and providing datathat instructs or causes the robot to traverse the space in theidentified pose or configuration.
 9. The system of claim 8, whereindetermining that the candidate path may result in a potential collisionis based on a maximum profile of the robot that represents a maximumdimension across multiple potential poses or configurations of therobot.
 10. The system of claim 8, wherein evaluating shapes of the robotthat correspond to different poses or configurations of the robotcomprises comparing a shape of a region between obstacles withthree-dimensional shapes of the robot that correspond to different posesor configurations of the robot.
 11. The system of claim 8, wherein therobot has an arm, and wherein the different poses or configurations ofthe robot comprises different positions of the arm with respect to therest of the robot.
 12. The system of claim 8, wherein determining thatthe candidate path may result in a potential collision is based on atwo-dimensional profile for the robot.
 13. The system of claim 8,wherein the shapes of the robot that correspond to different poses orconfigurations of the robot include three-dimensional shapes forapproaches by the robot in which different sides or portions of therobot face in the direction of travel of the robot.
 14. The system ofclaim 8, wherein accessing data indicating the layout comprisesreceiving map data from a server over a communication network.
 15. Oneor more non-transitory computer-readable media storing instructions thatare operable, when executed by the one or more computers, cause the oneor more computers to perform operations comprising: accessing dataindicating a layout of objects in a space; determining that a candidatepath for a robot to traverse the space may result in a potentialcollision; in response to the determination, evaluating shapes of therobot that correspond to different poses or configurations of the robot;based on the evaluation, identifying one of the poses or configurationsof the robot that enables the robot to traverse the space without acollision; and providing data that instructs or causes the robot totraverse the space in the identified pose or configuration.
 16. The oneor more non-transitory computer-readable media of claim 15, whereindetermining that the candidate path may result in a potential collisionis based on a maximum profile of the robot that represents a maximumdimension across multiple potential poses or configurations of therobot.
 17. The one or more non-transitory computer-readable media ofclaim 15, wherein evaluating shapes of the robot that correspond todifferent poses or configurations of the robot comprises comparing ashape of a region between obstacles with three-dimensional shapes of therobot that correspond to different poses or configurations of the robot.18. The one or more non-transitory computer-readable media of claim 15,wherein the robot has an arm, and wherein the different poses orconfigurations of the robot comprises different positions of the armwith respect to the rest of the robot.
 19. The one or morenon-transitory computer-readable media of claim 15, wherein determiningthat the candidate path may result in a potential collision is based ona two-dimensional profile for the robot.
 20. The one or morenon-transitory computer-readable media of claim 15, wherein the shapesof the robot that correspond to different poses or configurations of therobot include three-dimensional shapes for approaches by the robot inwhich different sides or portions of the robot face in the direction oftravel of the robot.